Seminars in Radiation Oncology
Volume 18, Issue 4 , Pages 234-239, October 2008

The Linear-Quadratic Model Is an Appropriate Methodology for Determining Isoeffective Doses at Large Doses Per Fraction

  • David J. Brenner, PhD, DSc

      Affiliations

    • Corresponding Author InformationAddress reprint requests to David J. Brenner, PhD, Center for Radiological Research, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032

Center for Radiological Research, Columbia University Medical Center, 630 West 168th Street, New York, NY

The tool most commonly used for quantitative predictions of dose/fractionation dependencies in radiotherapy is the mechanistically based linear-quadratic (LQ) model. The LQ formalism is now almost universally used for calculating radiotherapeutic isoeffect doses for different fractionation/protraction schemes. In summary, the LQ model has the following useful properties for predicting isoeffect doses: (1) it is a mechanistic, biologically based model; (2) it has sufficiently few parameters to be practical; (3) most other mechanistic models of cell killing predict the same fractionation dependencies as does the LQ model; (4) it has well-documented predictive properties for fractionation/dose-rate effects in the laboratory; and (5) it is reasonably well validated, experimentally and theoretically, up to about 10 Gy/fraction and would be reasonable for use up to about 18 Gy per fraction. To date, there is no evidence of problems when the LQ model has been applied in the clinic.

To access this article, please choose from the options below

Login to an existing account or Register a new account.

  • Purchase this article for 31.50 USD (You must login/register to purchase this article)

    Online access for 24 hours. The PDF version can be downloaded as your permanent record.

  • Subscribe to this title

    Get unlimited online access to this article and all other articles in this title 24/7 for one year.

  • Claim access now

    For current subscribers with Society Membership or Account Number.

  • Visit SciVerse ScienceDirect to see if you have access via your institution.
 

 Supported by NIH grants U19 AI-067773 and P41 RR-11623.

PII: S1053-4296(08)00033-7

doi:10.1016/j.semradonc.2008.04.004

Seminars in Radiation Oncology
Volume 18, Issue 4 , Pages 234-239, October 2008